A fast adaptive reduced rank transformation for minimum variance beamforming

Abstract

In conventional reduced rank minimum variance beamformer (RRMVB), a rank-reducing transformation is usually obtained from eigenvectors of the estimated sample covariance matrix, while the eigenvectors are usually obtained via eigen-decomposition. To alleviate the computational burden caused by eigen-decomposition, a fast reduced rank minimum variance beamformer (FRRMVB) is proposed in this paper. In the estimated covariance matrix case, a set of receive data vectors are taken as a rough and fast estimate of the true interference subspace, and the rank-reducing transformation is chosen as the augmentation of the estimated interference subspace with the steering vector of the desired signal. As FRRMVB performs without eigen-decomposition, it requires less computational load and is easier to be executed in practical applications compared with the conventional RRMVB. Moreover, it has good performance even with small sample size. Simulation results demonstrate the efficiency of the proposed method. The proposed method can be used for real-time adaptive array processing.